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This paper identifies a capacity-induced failure mode in physics-informed neural networks (PINNs) where overparameterized networks develop functional modularity that hinders convergence, and proposes Modular-Sparsity Synchronization (ModSync), a framework that penalizes task-exclusive connections to maintain cross-objective interaction and achieve state-of-the-art accuracy.
This article draws parallels between biological evolution and technological evolution, explaining how modularity and sexual reproduction allow populations to increase the rate of information acquisition. Simulations demonstrate that mixing genetic material accelerates the spread of beneficial mutations, analogous to how technologies build on existing components.
LatentSkill converts textual skills into LoRA adapters stored in weight space, reducing context overhead while maintaining modularity and composability for LLM agents, achieving significant improvements on ALFWorld and Search-QA benchmarks.
This paper proposes principled approaches for designing and optimizing practical agentic LLM systems, introducing a framework with pseudo-tools and fixed workflows to improve modularity, cost-efficiency, and accuracy across diverse tasks.
Allen AI releases EMO, a mixture-of-experts model where modular structure emerges naturally from data, enabling use of just 12.5% of experts for a task while maintaining near full-model performance.